Simulation-Based Optimization of Path Planning for Camera-Equipped UAVs That Considers the Location and Time of Construction Activities
Bibliographic record
Abstract
Automated progress monitoring of construction sites using cameras has been proposed in recent years. Although previous studies have tried to identify the most informative camera views according to 4D BIM to optimize installation plans, video collection using fixed or pan-tilt-zoom cameras is still limited by their inability to adapt to the dynamic construction environment. Therefore, considerable attention has been paid to using camera-equipped unmanned aerial vehicles (CE-UAVs), which provide mobility for the camera, allowing it to fit its field of view automatically to the important parts of the construction site while avoiding occlusions. However, previous studies on optimizing video collection with CE-UAV are limited to the scanning of static objects on construction sites. Given the growing interest in construction activities, the existing methods are inadequate to meet the requirements for the collection of high-quality videos. In this study, the following requirements for and constraints on collecting construction-activity videos have been identified: (1) the FOV should be optimized to cover the areas of interest with the minimum possible occlusion; (2) the path of the UAV should be optimized to allow efficient data collection on multiple construction activities over a large construction site, considering the locations of activities at specific times; and (3) the data collection should consider the requirements for CV processes. Aiming to address these requirements and constraints, a method has been proposed to perform simulation-based optimization of path planning for CE-UAVs to allow automated and effective collection of videos of construction activities based on a detailed 4D simulation that includes a micro-schedule and the corresponding workspaces. This method can identify the most informative views of the workspaces and the optimal path for data capture. A case study was developed to demonstrate the feasibility of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".